SCIENTIFIC CAMERA CAPABILITIES AND LIMITATIONS, WITH CONSIDERATIONS FOR LIGHTSHEET AND LOCALIZATION MICROSCOPIES

 scientific camera, tutorial, , read noise, excess noise, fixed pattern noise, relative signal to noise ratio, lightsheet microscopy, localization microscopy, super resolution microscopy, maximum likelihood estimation.

No scientific camera is perfect and no camera is ideal for every application; there are always tradeoffs between noise, field of view, resolution, speed and throughput, full well capacity, dynamic range, quantum efficiency, modulation transfer function and pixel uniformity. A “scientific” camera is a scientific instrument and understanding the precision and accuracy of measurements facilitates high quality research.

The architectures and limitations of CCD, EMCCD and sCMOS camera technologies will be described, emphasizing the relevance of these differences for life science research, including a discussion of lightsheet and super resolution localization microscopies. The single-frame signal to noise ratio relative that of a perfect camera (rSNR) as a function of the light intensity directly shows the effects of multiple camera noises (see Figure). Measured and modelled output probability distribution functions for uniform light input will be discussed to illustrate the statistical nature of camera output, particularly the contributions of read noise and electron multiplication (excess) noises. New functionality for controlling and synchronizing the camera shutter with illumination and excitation in scientific CMOS cameras (sCMOS), such as lightsheet modeTM, and global exposure timing bring previously unavailable capabilities to researchers. Measured and modelled output probability distribution functions for uniform light input will be discussed to illustrate the statistical nature of camera output, particularly the contributions of read noise and electron multiplication (excess) noises. Localization microscopy will be used as an example to show how understanding camera noise characteristics can be used to improve results in computational imaging, in particular the interaction between camera-induced noises and maximum likelihood estimation .

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